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Selecting products through text reviews: An MCDM method incorporating personalized heuristic judgments in the prospect theory

Author

Listed:
  • Meng Zhao

    (Northeastern University
    Sichuan University
    Northeastern University at Qinhuangdao)

  • Xinyuan Shen

    (Peking University)

  • Huchang Liao

    (Sichuan University)

  • Mingyao Cai

    (Carleton University)

Abstract

Online reviews have become an increasingly popular information source in consumer’s decision making process. To help consumers make informed decisions, how to select products through online reviews is a valuable research topic. This work deals with a personized product selection problem with review sentiments under probabilistic linguistic circumstances. To this end, we propose a multi-criteria decision making (MCDM) method incorporating personalized heuristic judgments in the prospect theory (PT). We focus on the role of personalized heuristic judgments on review helpfulness in the final decision outcomes. We demonstrate the consistency between the three common heuristic judgments (with respect to review valence, sentiment extremity, and aspiration levels) and the three behavioral principles of the PT. Then, the products are ranked with the probabilistic linguistic term set (PLTS) input, based on the proposed adjustable PT framework, in which the coefficients of negativity bias are derived from the consumer’s heuristic judgments. Finally, a real case on TripAdvisor.com and two simulation experiments are given to illustrate the validity of the proposed method.

Suggested Citation

  • Meng Zhao & Xinyuan Shen & Huchang Liao & Mingyao Cai, 2022. "Selecting products through text reviews: An MCDM method incorporating personalized heuristic judgments in the prospect theory," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 21-44, March.
    • Handle: RePEc:spr:fuzodm:v:21:y:2022:i:1:d:10.1007_s10700-021-09359-8
    DOI: 10.1007/s10700-021-09359-8
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    References listed on IDEAS

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    1. Dong Zhang & Chong Wu & Jiaming Liu, 2020. "Ranking products with online reviews: A novel method based on hesitant fuzzy set and sentiment word framework," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(3), pages 528-542, March.
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    5. Yang Liu & Jian-Wu Bi & Zhi-Ping Fan, 2017. "A Method for Ranking Products Through Online Reviews Based on Sentiment Classification and Interval-Valued Intuitionistic Fuzzy TOPSIS," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1497-1522, November.
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    Cited by:

    1. Xiaohong Chen & Hui Wang & Xihua Li, 2024. "Doctor recommendation under probabilistic linguistic environment considering patient’s risk preference," Annals of Operations Research, Springer, vol. 341(1), pages 555-581, October.
    2. Pingping Cao & Jin Zheng & Mingyang Li, 2023. "Product Selection Considering Multiple Consumers’ Expectations and Online Reviews: A Method Based on Intuitionistic Fuzzy Soft Sets and TODIM," Mathematics, MDPI, vol. 11(17), pages 1-20, September.

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